{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0bad6d6a",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ad46a5d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "from pandas.errors import SettingWithCopyWarning\n",
    "warnings.simplefilter(action=\"ignore\",category=SettingWithCopyWarning)\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder  \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_auc_score\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa3ad7d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises?</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>poisonous</td>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>brown</td>\n",
       "      <td>bruises</td>\n",
       "      <td>pungent</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>narrow</td>\n",
       "      <td>black</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>black</td>\n",
       "      <td>scattered</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>edible</td>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>yellow</td>\n",
       "      <td>bruises</td>\n",
       "      <td>almond</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>broad</td>\n",
       "      <td>black</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>brown</td>\n",
       "      <td>numerous</td>\n",
       "      <td>grasses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>edible</td>\n",
       "      <td>bell</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>bruises</td>\n",
       "      <td>anise</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>broad</td>\n",
       "      <td>brown</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>brown</td>\n",
       "      <td>numerous</td>\n",
       "      <td>meadows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>poisonous</td>\n",
       "      <td>convex</td>\n",
       "      <td>scaly</td>\n",
       "      <td>white</td>\n",
       "      <td>bruises</td>\n",
       "      <td>pungent</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>narrow</td>\n",
       "      <td>brown</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>black</td>\n",
       "      <td>scattered</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>edible</td>\n",
       "      <td>convex</td>\n",
       "      <td>smooth</td>\n",
       "      <td>gray</td>\n",
       "      <td>no</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>crowded</td>\n",
       "      <td>broad</td>\n",
       "      <td>black</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>evanescent</td>\n",
       "      <td>brown</td>\n",
       "      <td>abundant</td>\n",
       "      <td>grasses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Class cap-shape cap-surface cap-color bruises?     odor  \\\n",
       "0  poisonous    convex      smooth     brown  bruises  pungent   \n",
       "1     edible    convex      smooth    yellow  bruises   almond   \n",
       "2     edible      bell      smooth     white  bruises    anise   \n",
       "3  poisonous    convex       scaly     white  bruises  pungent   \n",
       "4     edible    convex      smooth      gray       no     none   \n",
       "\n",
       "  gill-attachment gill-spacing gill-size gill-color  ...  \\\n",
       "0            free        close    narrow      black  ...   \n",
       "1            free        close     broad      black  ...   \n",
       "2            free        close     broad      brown  ...   \n",
       "3            free        close    narrow      brown  ...   \n",
       "4            free      crowded     broad      black  ...   \n",
       "\n",
       "  stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring  \\\n",
       "0                   smooth                  white                  white   \n",
       "1                   smooth                  white                  white   \n",
       "2                   smooth                  white                  white   \n",
       "3                   smooth                  white                  white   \n",
       "4                   smooth                  white                  white   \n",
       "\n",
       "  veil-type veil-color ring-number   ring-type spore-print-color population  \\\n",
       "0   partial      white         one     pendant             black  scattered   \n",
       "1   partial      white         one     pendant             brown   numerous   \n",
       "2   partial      white         one     pendant             brown   numerous   \n",
       "3   partial      white         one     pendant             black  scattered   \n",
       "4   partial      white         one  evanescent             brown   abundant   \n",
       "\n",
       "   habitat  \n",
       "0    urban  \n",
       "1  grasses  \n",
       "2  meadows  \n",
       "3    urban  \n",
       "4  grasses  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Mushroom.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b106213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Class',\n",
       " 'cap-shape',\n",
       " 'cap-surface',\n",
       " 'cap-color',\n",
       " 'bruises?',\n",
       " 'odor',\n",
       " 'gill-attachment',\n",
       " 'gill-spacing',\n",
       " 'gill-size',\n",
       " 'gill-color',\n",
       " 'stalk-shape',\n",
       " 'stalk-root',\n",
       " 'stalk-surface-above-ring',\n",
       " 'stalk-surface-below-ring',\n",
       " 'stalk-color-above-ring',\n",
       " 'stalk-color-below-ring',\n",
       " 'veil-type',\n",
       " 'veil-color',\n",
       " 'ring-number',\n",
       " 'ring-type',\n",
       " 'spore-print-color',\n",
       " 'population',\n",
       " 'habitat']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be4cfdb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class\n",
      "edible       4208\n",
      "poisonous    3916\n",
      "Name: count, dtype: int64\n",
      "cap-shape\n",
      "convex     3656\n",
      "flat       3152\n",
      "knobbed     828\n",
      "bell        452\n",
      "sunken       32\n",
      "conical       4\n",
      "Name: count, dtype: int64\n",
      "cap-surface\n",
      "scaly      3244\n",
      "smooth     2556\n",
      "fibrous    2320\n",
      "grooves       4\n",
      "Name: count, dtype: int64\n",
      "cap-color\n",
      "brown       2284\n",
      "gray        1840\n",
      "red         1500\n",
      "yellow      1072\n",
      "white       1040\n",
      "buff         168\n",
      "pink         144\n",
      "cinnamon      44\n",
      "purple        16\n",
      "green         16\n",
      "Name: count, dtype: int64\n",
      "bruises?\n",
      "no         4748\n",
      "bruises    3376\n",
      "Name: count, dtype: int64\n",
      "odor\n",
      "none        3528\n",
      "foul        2160\n",
      "fishy        576\n",
      "spicy        576\n",
      "almond       400\n",
      "anise        400\n",
      "pungent      256\n",
      "creosote     192\n",
      "musty         36\n",
      "Name: count, dtype: int64\n",
      "gill-attachment\n",
      "free        7914\n",
      "attached     210\n",
      "Name: count, dtype: int64\n",
      "gill-spacing\n",
      "close      6812\n",
      "crowded    1312\n",
      "Name: count, dtype: int64\n",
      "gill-size\n",
      "broad     5612\n",
      "narrow    2512\n",
      "Name: count, dtype: int64\n",
      "gill-color\n",
      "buff         1728\n",
      "pink         1492\n",
      "white        1202\n",
      "brown        1048\n",
      "gray          752\n",
      "chocolate     732\n",
      "purple        492\n",
      "black         408\n",
      "red            96\n",
      "yellow         86\n",
      "orange         64\n",
      "green          24\n",
      "Name: count, dtype: int64\n",
      "stalk-shape\n",
      "tapering     4608\n",
      "enlarging    3516\n",
      "Name: count, dtype: int64\n",
      "stalk-root\n",
      "bulbous    3776\n",
      "equal      1120\n",
      "club        556\n",
      "rooted      192\n",
      "Name: count, dtype: int64\n",
      "stalk-surface-above-ring\n",
      "smooth    5176\n",
      "silky     2372\n",
      "ibrous     552\n",
      "scaly       24\n",
      "Name: count, dtype: int64\n",
      "stalk-surface-below-ring\n",
      "smooth    4936\n",
      "silky     2304\n",
      "ibrous     600\n",
      "scaly      284\n",
      "Name: count, dtype: int64\n",
      "stalk-color-above-ring\n",
      "white       4464\n",
      "pink        1872\n",
      "gray         576\n",
      "brown        448\n",
      "buff         432\n",
      "orange       192\n",
      "red           96\n",
      "cinnamon      36\n",
      "yellow         8\n",
      "Name: count, dtype: int64\n",
      "stalk-color-below-ring\n",
      "white       4384\n",
      "pink        1872\n",
      "gray         576\n",
      "brown        512\n",
      "buff         432\n",
      "orange       192\n",
      "red           96\n",
      "cinnamon      36\n",
      "yellow        24\n",
      "Name: count, dtype: int64\n",
      "veil-type\n",
      "partial    8124\n",
      "Name: count, dtype: int64\n",
      "veil-color\n",
      "white     7924\n",
      "brown       96\n",
      "orange      96\n",
      "yellow       8\n",
      "Name: count, dtype: int64\n",
      "ring-number\n",
      "one     7488\n",
      "two      600\n",
      "none      36\n",
      "Name: count, dtype: int64\n",
      "ring-type\n",
      "pendant       3968\n",
      "evanescent    2776\n",
      "large         1296\n",
      "flaring         48\n",
      "none            36\n",
      "Name: count, dtype: int64\n",
      "spore-print-color\n",
      "white        2388\n",
      "brown        1968\n",
      "black        1872\n",
      "chocolate    1632\n",
      "green          72\n",
      "purple         48\n",
      "orange         48\n",
      "yellow         48\n",
      "buff           48\n",
      "Name: count, dtype: int64\n",
      "population\n",
      "several      4040\n",
      "solitary     1712\n",
      "scattered    1248\n",
      "numerous      400\n",
      "abundant      384\n",
      "clustered     340\n",
      "Name: count, dtype: int64\n",
      "habitat\n",
      "woods      3148\n",
      "grasses    2148\n",
      "paths      1144\n",
      "leaves      832\n",
      "urban       368\n",
      "meadows     292\n",
      "waste       192\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns.to_list():\n",
    "    print(df[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ff32797",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises?</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>...</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>edible</td>\n",
       "      <td>convex</td>\n",
       "      <td>scaly</td>\n",
       "      <td>brown</td>\n",
       "      <td>no</td>\n",
       "      <td>none</td>\n",
       "      <td>free</td>\n",
       "      <td>close</td>\n",
       "      <td>broad</td>\n",
       "      <td>buff</td>\n",
       "      <td>...</td>\n",
       "      <td>smooth</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>partial</td>\n",
       "      <td>white</td>\n",
       "      <td>one</td>\n",
       "      <td>pendant</td>\n",
       "      <td>white</td>\n",
       "      <td>several</td>\n",
       "      <td>woods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4208</td>\n",
       "      <td>3656</td>\n",
       "      <td>3244</td>\n",
       "      <td>2284</td>\n",
       "      <td>4748</td>\n",
       "      <td>3528</td>\n",
       "      <td>7914</td>\n",
       "      <td>6812</td>\n",
       "      <td>5612</td>\n",
       "      <td>1728</td>\n",
       "      <td>...</td>\n",
       "      <td>4936</td>\n",
       "      <td>4464</td>\n",
       "      <td>4384</td>\n",
       "      <td>8124</td>\n",
       "      <td>7924</td>\n",
       "      <td>7488</td>\n",
       "      <td>3968</td>\n",
       "      <td>2388</td>\n",
       "      <td>4040</td>\n",
       "      <td>3148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Class cap-shape cap-surface cap-color bruises?  odor gill-attachment  \\\n",
       "count     8124      8124        8124      8124     8124  8124            8124   \n",
       "unique       2         6           4        10        2     9               2   \n",
       "top     edible    convex       scaly     brown       no  none            free   \n",
       "freq      4208      3656        3244      2284     4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "count          8124      8124       8124  ...                     8124   \n",
       "unique            2         2         12  ...                        4   \n",
       "top           close     broad       buff  ...                   smooth   \n",
       "freq           6812      5612       1728  ...                     4936   \n",
       "\n",
       "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "count                    8124                   8124      8124       8124   \n",
       "unique                      9                      9         1          4   \n",
       "top                     white                  white   partial      white   \n",
       "freq                     4464                   4384      8124       7924   \n",
       "\n",
       "       ring-number ring-type spore-print-color population habitat  \n",
       "count         8124      8124              8124       8124    8124  \n",
       "unique           3         5                 9          6       7  \n",
       "top            one   pendant             white    several   woods  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f037a127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c0207d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class                          0\n",
      "cap-shape                      0\n",
      "cap-surface                    0\n",
      "cap-color                      0\n",
      "bruises?                       0\n",
      "odor                           0\n",
      "gill-attachment                0\n",
      "gill-spacing                   0\n",
      "gill-size                      0\n",
      "gill-color                     0\n",
      "stalk-shape                    0\n",
      "stalk-root                  2480\n",
      "stalk-surface-above-ring       0\n",
      "stalk-surface-below-ring       0\n",
      "stalk-color-above-ring         0\n",
      "stalk-color-below-ring         0\n",
      "veil-type                      0\n",
      "veil-color                     0\n",
      "ring-number                    0\n",
      "ring-type                      0\n",
      "spore-print-color              0\n",
      "population                     0\n",
      "habitat                        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "984f049f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class                       object\n",
      "cap-shape                   object\n",
      "cap-surface                 object\n",
      "cap-color                   object\n",
      "bruises?                    object\n",
      "odor                        object\n",
      "gill-attachment             object\n",
      "gill-spacing                object\n",
      "gill-size                   object\n",
      "gill-color                  object\n",
      "stalk-shape                 object\n",
      "stalk-root                  object\n",
      "stalk-surface-above-ring    object\n",
      "stalk-surface-below-ring    object\n",
      "stalk-color-above-ring      object\n",
      "stalk-color-below-ring      object\n",
      "veil-type                   object\n",
      "veil-color                  object\n",
      "ring-number                 object\n",
      "ring-type                   object\n",
      "spore-print-color           object\n",
      "population                  object\n",
      "habitat                     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e0d5681d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9157dc83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['brown', 'gray', 'red', 'yellow', 'white', 'buff', 'pink', 'cinnamon', 'purple', 'green']\n",
      "[2284, 1840, 1500, 1072, 1040, 168, 144, 44, 16, 16]\n"
     ]
    }
   ],
   "source": [
    "cap_colors = df['cap-color'].value_counts() \n",
    "ccy = cap_colors.values.tolist()\n",
    "ccx = cap_colors.axes[0].tolist()\n",
    "print(ccx)\n",
    "print(ccy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f1e53e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'brown'),\n",
       " Text(1, 0, 'gray'),\n",
       " Text(2, 0, 'red'),\n",
       " Text(3, 0, 'yellow'),\n",
       " Text(4, 0, 'white'),\n",
       " Text(5, 0, 'buff'),\n",
       " Text(6, 0, 'pink'),\n",
       " Text(7, 0, 'cinnamon'),\n",
       " Text(8, 0, 'purple'),\n",
       " Text(9, 0, 'green')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#DEB887', '#778899', '#DC143C', '#FFFF99', '#f8f8ff', '#F0DC82', '#FF69B4', '#D22D1E', '#C000C5', '#008000']\n",
    "fig,ax = plt.subplots(figsize=(10,7))\n",
    "cap_colors_bar = ax.bar(range(10),ccy,width=0.7,color=colors)\n",
    "ax.set_xlabel('蘑菇帽颜色',fontsize=16)\n",
    "ax.set_ylabel('数量',fontsize=16)\n",
    "ax.set_title('蘑菇帽颜色-数量',fontsize=16)\n",
    "\n",
    "ax.set_xticks(range(10))\n",
    "ax.set_xticklabels(ccx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ea7be04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1264, 1032, 624, 400, 720, 48, 56, 32, 16, 16]\n",
      "[1020, 808, 876, 672, 320, 120, 88, 12, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "poisonous_cc = []\n",
    "edible_cc = []\n",
    "\n",
    "for i in ccx:\n",
    "    size = len(df[df['cap-color'] == i].index)  # 各颜色蘑菇总数\n",
    "    edibles = len(df[(df['cap-color'] == i) & (df['Class'] == 'edible')].index)  # 各颜色食用菌的数量\n",
    "    edible_cc.append(edibles)\n",
    "    poisonous_cc.append(size - edibles)   \n",
    "print(edible_cc)\n",
    "print(poisonous_cc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "953cd2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YXylrlTDJfdhw/Phxj685LzwNeXILNXLy3nvv6eDBg5Kk999/P19ncGVmZhbIjDBPz5P1+9KkSRPde++9Tm0XL17U7NmzZbFYHKs7ujJp0iQFBASod+/estvtyszMlM1mc1lMO8uRI0f0/PPPq0OHDmrevLnCwsIcxfddhZ/R0dGKj49X165d1aFDBz377LN65pln3J4HAFC4CJUAAMVW1apVPa6pVNz07t1b5cuXd3yh+uyzz2S32/X666/rlltu0YULF1SiRAn5+PioRIkSunjxop5++mmXs0Wio6N18eLFHOuUjBkzRiNGjJDdbpd0qQbRoUOHdMcdd7gdY2JiomMZ+W+//TZbbSdJqlu3rtOsqitd/sXU19c3x9kmWeFIVmgwYsQIZWZmKiIiQk899ZTLY2fVXfEkbMh6Dz2RH8udf//99+rfv7/j82uxWJSSkuJ4DMtTKSkpqly5si5cuKCEhATNnz9f8+fPV7ly5fTggw9q9uzZOYZLWZ+TlJQU7d+/P9fzeNLHnb/++kv+/v7ZaoVlZGRI+t9KatKlsC0lJUWpqakqXbq0x2GR2QDnwIEDeueddyRdej9chZPudO7cWf/6179ybGvfvr3Wrl0rPz8/+fv75xpuuqupdDnDMByroNlsNnXt2lWff/55tn5Zv0vt27fXiBEjnNoOHTrk+GzkVptp0qRJCgwMNF3Daffu3Y5HUx955BF98803WrhwYa77WSwWff/995KkVq1amTonAODaI1QCABRbx48fz/VxjawC0p7IyMjQxYsXHcV0vb298/WRHrvd7ii0nZycrICAAKdHs/766y9VqFDBaZ93333X6ecxY8ZozJgxkqSNGzeqcePGji+r8+fPd/kFvF69erp48WKOoU2vXr20du1arVmzRm+99ZZSU1M1duxYU9d2/vz5HLfnFgiYeYzFy8tLmzZtcnwxHTZsmNsgISuw8CSUyHoPZ86cqeeeey7HPjVr1tTx48evahnzH3/8UcOGDdNvv/0m6dI1DR8+XGvWrNHPP//s0SNCV+rYsaMmTZqkUaNGadasWcrIyNCZM2dUr149l++v8f/LxTdt2tRlPaqsItqX989Js2bNtGHDBrdjbN26tdtg6ueff87xscx58+blqQ5Tbmw2m5555hlHkGUYhs6cOZOn47hitVoVHByswMBAWa3WXEOvnELZnFx+L9zVV8r63FssFg0ePFinTp1yBHsJCQmSLv1N6tu3r2w2m4KCgjR58mSNHz9ehw4dkr+/v2PMSUlJ6t+/v5KSknTx4kVdvHhRI0aMUPPmzV2Oc/PmzY5/v/HGGx5dmySnz0FOxbwBAEULoRIAoNhKT0+/6hkUlzt06JBq166dp32PHTtmOoD68MMPnYoCXz4DJjQ01OWXxazCwFmufAzlwoUL+vPPP1WiRAnHKltZcgpYypQpo2+//VYbN25UmzZtNGzYMElS5cqVdfTo0Wz9MzIyHMdcu3atWrZsma1Pt27dNH/+fLcFxSXzdXgGDhwou92u22+/XS+88ILbvlkzlTz5Ypp1PT4+Pi5r82SNNS+PWWWx2+3atm2bJOmmm27SvHnzdP/99ztWjBs6dKjblewu1717d3322WeSLtW3+fDDDzVkyBB99NFH+vLLLx2BUE7chUQFwd/fX2XKlHEKKqRL9cAyMzOdVjHMyMhwfMYLqk5T//79HY96tm/fPsei1O5Ur15df/zxh8vPipRzoetrKevvh7e3t1avXq3Dhw/L399fsbGxjkeCDcPQtGnTJEnVqlXT5MmTtWHDBq1bt05JSUmO2YupqanZZmT16dPH6eedO3cqKSlJ/v7+CggI0LJlyyRdCsvKly+fbVZpVnh/4MABBQcHy9fX1/G75e3trczMTF24cMGxX0ZGhlJTU5WSkiJ/f3/ddddd+fl2AQDyiFAJAFBsVatWLdfH34YNG+aY2VPUBQUFOVZ4+u9//+tUd+T06dN66aWXNG3aNEVERDi+7OXku+++cwQue/bs8SgoCwwMVJs2ba7+Iq6Q3zMNvv/+e7322mvq0qWLvLy8tHjxYiUkJCgyMjJbXzOh0rXSvn17vfHGGzpy5IimTZumMmXK5OvxK1Wq5DSbzRV3n5+CkLUi4eXOnz+vcuXKSZIaN27sCNau5Gp7Xk2ePNntKmVmmJ211rJlS6fi1nlxyy23aM2aNbn2uzxU2rJli6RLj9hFREQoODhYZ8+elbe3t86dO+f4XZEu1V27ePGiatasqYsXLyotLU3+/v7y8vJSRESE435cWWT/zTffdKzOeLnTp0+rRo0a2ba/8MIL6tSpk6MofU5GjhypkSNHZtvuboYdAODaIlQCAOD/RURE6PDhw06Pv+X2eNYrr7yi+fPnq1KlStq9e7fbvllFbjMyMpSUlJStyLjFYlFAQICSk5OdZsMcPnxYDz30kPbv36+dO3fq999/d7s6UlbBaR8fnxy/zF1L+b1KU6lSpTRv3jxJUr9+/fT++++rXLlyeuaZZ7LNGklLS5Mkt7NJCsPo0aNdvi9Tp071uAj45YW0zbo8RCgsH3/8cbbi6H///bdKly5dYAWuZ82aZepRrNyYmbWWnp6uX375xeOC8K54GkRmzUbLCr4Mw9CLL76oixcv6tNPP1X37t0lXZpxtHPnTi1atMhRmL9Hjx76+++/NWXKFA0YMEBBQUGKiorS4MGD1bFjRy1evNixKmUWq9WqkiVLKjAwUKmpqYqNjZV0aYXGy+/n8ePHZRiGatSooTJlyqhVq1aO2U3+/v6yWCxasGCB7Ha77rnnHsffsIyMDEetrVtvvTXvbyAAIF8RKgEAiqQzZ86oevXqjhogfn5+jkeUsr5Me1JT6dy5c5Kk3377zalvVriTkpKimjVrat26dfL19fV4SfYsl9ctyakItiuuVlXz9fV1CpWWLl2qHj166MKFC6pRo4YWLVqU63LbWYFBuXLlrupRrfxg5jErs0V5u3btqvfff19nzpzRrFmz9I9//MOpvSjOVJLcB21JSUmO4ueeysujbFn1pgpLXFxctsLP69atc6z69dFHH+X7OadPn66+ffvKMAyVLl1afn5++uuvv67qmGZC0wMHDjgCpdOnTztmaUmX/t498sgjqlevnt555x3ddNNN2fbv2bOnPv74Y4//RmXNRvP395fdblfv3r21dOlS9enTR02aNJF06bPTq1cvPfnkk/r00081YcIE9evXT0uWLFGPHj3Ur18/DRgwQJI0aNAgHTp0SLNmzVLt2rU1fPhw9e7d21EX7rvvvnOc+5VXXtFHH32k8uXL69ixY473KT4+3vH3q27dumrWrFmOs64WL17sWFXy8keEAQBFD6ESAKBIMgxDiYmJbgsXm6mp5G6Vq4IoBJxXWV86Y2NjNXDgQC1YsECS9PTTT2vWrFlKTEzUli1b1LBhQ5fHSE5OliSnL62F5WpnZbjTqFEjtW7dWmvWrNGkSZP06quvOs2IyKobU9RCJXfyUlMpL+9x1nuzefNmlzNfLi9C7W52TF5mTI0aNSpbcfe1a9fqr7/+0syZM1WlShUNGTLE9HFzkpmZqcGDBztCrIoVK2rlypXq3r27/vrrL0VHRxdY7abL7dixQ9Klx9eu/N1cv3699u/fr99++03z58/XtGnTsj3SmfV+VapUyaPzZc3UCwwM1IABA/TRRx/pnnvu0aRJk3Tw4EFJl4Knxo0ba/v27QoODlabNm20du1ade3aVR999JHjM5AV0H788ccqX7683nnnHUVFRclisWjgwIHZzrt48WJJcjymmmXbtm2OELRx48YeXQcAoGgjVAIAFEllypTRzp07FRwc7HhlrQp04MAB3XPPPYqLi1Pfvn1dLuktSW+//bZGjhyp8PBwRUdHq06dOpIuffFJS0tTXFycYwWooiBrLEePHtVXX32lgIAATZw4Ub1799bp06fVqlUrHT58WBMnTlTfvn1zPEZWqOTpalKueFJ8PLfZRWbe2+joaLVo0SLb9hdffNERrl1pwIABWrNmjeP9euqppxxtWV+IzYRK6enpLsd8rYtbeyovj7JlzYbKyMhwuXLf5Tzp46mYmBhNnz5dktSwYUNHvZ+RI0dq9+7dWrx4sYYOHar69eurffv2V3WuU6dO6bnnntPq1aslSXXq1NGKFStUsWJFRx8fHx+nIvmeiI2NdVuXyjCMbL87MTExkpRjgemnnnpKzZo10/PPP6/4+Hh16tQpW5+sWZeXj93VuaT/3ePQ0FBNnDhRVatWVbdu3eTn5yebzeZYaTIxMVG33HKLJGn27NlatGiRoqKi5OXlpaSkJFWoUEFeXl7KzMyUt7e33n77bdWpU0cZGRnq1q1btvN+++23js/LlSsp/vjjj5IuzVJyNVsTAFC8ECoBAIokq9WqO+64I9v2rVu3qmPHjoqLi1Pjxo2dHqGpXLmyMjMz1bNnT40YMUKSNHz4cG3atEnLly9X8+bNtXTpUrVo0UK+vr7y9fU19chaQUtNTVV6erp8fX310EMP6dtvv1XFihVVu3Zt/fnnn2rbtq3279+vChUqqG7duo79rgw7sh7pyfqieDkzwYjValW1atXydC2nTp1SfHy8Y+lyT/j6+uZY/8jdI0YPPvigwsPDdfLkSb3//vtOoVLWbBwzNZV69uypnj17etw/v+WlplJeQtGsgOLRRx/V119/nWOfWbNmOVaQc/e5adasmTZs2ODReRMTE9W1a1dlZGSofv366tGjhyNUslgs+uyzz7Rz504dOHBA3bt31++//27iqpylp6frxRdfdARKnTt31qxZs7L9zrdu3TrPq7+5snnzZjVr1kwlSpRwhJpZ92vNmjUuA1/DMJSenp5jcf2soGbw4MEaNWqU7Ha7UlJSnFZpu9ypU6ckSaVLl9a5c+e0e/dujRo1yvFI7JNPPinpUvB+pctnH2X1GzBggDIzM5Wenq7k5GT985//zPEaPvjgA0nS7bffni1Ay/qsPfHEE45tTZs2zfZeZgXCgwYN0qhRo5zaBg0a5HgkDwBQ+AiVAADFQmpqqiZMmKCxY8fKZrPpjjvu0FdffeWosyRdqtdx8eJFp2XsLRaLFi1apIceeki//PKLWrVqpTfffFMjRoxwzHwqKv7++29JUsmSJSVJ999/vyRp3759euCBB3Ts2DHdfvvtWrFihWOWgfS/8CTL8ePHJSnHelNmaulUqFBB+/btM3cR/69bt26aP3++zp49m6f9PeXt7a0XXnhBY8aM0S+//KKdO3c6AresGVtXrlJ1tQqyHlFeaiolJycrMTHRsXqgJ7K+xF/rgse9e/fWwYMH5ePjo1mzZum///2vU3tgYKA++ugjtWrVSqdPn9aCBQtyDJc94ePjox9++EH//ve/ZRhGtppbBcliscjHx0dWq1VWq1UZGRmO8C84ODhbrTO73S6bzaaAgACnv2lX9pEuzbyzWq2y2+3y8fFx2T8rVCpfvrzi4uI8Dis91aNHD8fMzyz//e9/HavDxcfHa+XKlY6/Yxs2bNDu3btlsVgcq1NKlz6/Z86cyfEc8fHxio+Pd9p2rVcuBAC4l79LsgAAkM8uXryocePGqUqVKho5cqRsNpsaN26sdevWqXz58k59s2YEXDkToUSJEvr+++/Vpk0b2e12jR8/XhUrVtSIESO0d+/ea3YtuckKlS4vxL1w4ULdc889OnbsmFq0aKFffvlFFSpUcAo2Lg+VDMPQ5s2bJUl33313tnNcGUAVlM8//1yGYWjr1q0Ffq7nnntOPj4+ioqKcqo3kxUqmXm06dNPP5VhGDm+KleuLClvM4M8NXToUJfnd/X64Ycf9PDDD+uJJ57wKJDKyMjQkSNHJCnHGTEF5cMPP3Ss3Dd8+PAcHwOTpJYtW+q1117T4sWL9dprr13VOS0Wi/r06eM2UFq7dq1uvvlmU6+jR4+6PW+jRo2UkpKis2fP6uTJk/rss88cbTt27NDJkyedXr1795ZhGHrhhRdybD958qRjFbc1a9bo5MmT+uuvv3ThwgWn2ldZEhISdPHiRfn6+qpy5cqqXr26zp8/L5vNJrvd7vjsZM1Suvvuu5WRkeH2c5aRkSGbzabExET9/fffjmLfl7vrrrv02WefqXLlyjp+/Ljat2+vp59+WqdOndKYMWMkSe3bt3f8Lkn/+7ud22c/a5+itpojANzomKkEACiS1qxZo2nTpik6OtoRhHh7e6t///6yWq0qXbq0AgICFBgY6CjOnDUr5o033tDw4cMlXQoAkpOTVa1aNf3+++8aPny4JkyYoPPnz+udd97RO++8o4iICMXExCg0NLRwLvb/HThwQNL/aqY899xz+vzzzyVJnTp10oIFC+Tv76/58+dr9OjR+vrrr1WzZk2VLFlSFSpUkMVi0W+//aa///5b/v7+atCggePYJUqUUMmSJXP8Alrc1axZUwcOHFCVKlWctmfNcPAkVMoqauzOXXfdpZtvvvma14J56aWXdOrUKQ0cOFBt2rRxaouLi9Ojjz6q9evXS5L+8Y9/6NNPP3V7vI0bNzqut3nz5gUz6CvMmzdPffr0kXQpNHL16FSW999/P9u2U6dO5fjY2NUGpWlpaS5nyuSXrBlZlSpVyhaGJyYmav78+YqNjdXYsWM1ZcoUrVq1yim0SU5OdoSZpUuXzvV827dvlyTVqFHD8fcxLCzMqc+SJUs0atQo+fj46JNPPnEqcp8Tb29veXt7y9fX1+XsPy8vLz3//PPq3Lmz3n33XY0ZM0b/+c9/tHz5cseiC1FRUU77uJpp5Uphr2gJAHDGTCUAQJFUqlQpLVu2zPGFsW7dutqwYYMmTpwof39/GYah5ORknTt3TmfOnNGZM2ccj0XEx8c7tsXFxSk9PV2GYchqtWrcuHFatWqV07LcDz/8cKEHSpK0e/duSVJERIQkacSIEbr11lv15ptvasmSJfL399fChQv1wgsvaN++fXrssceUkJCg06dP69ChQ/L29tbYsWMlSQ899JCCgoIcNY1+/vlnXbx4UQ8++GDhXFwBuzJQki7NcpM8C5XKli2ratWque27dOlSbdq0SUOHDs3rME1bs2aNPvnkEy1fvlxt27bVXXfdpfnz5ztmGzVr1kzr1q2Tl5eXRowYoY8//jjXY65cuVLSpfCyevXqBX0J+uabbxQZGSnDMFSuXDl98cUXuQYYObHb7Y7f68tfVz4eZVb79u1Nzw4zW2ssq27Uvffem60tODhYu3bt0ty5c1WpUiV169Yt2yygCxcuOP59ZTiUk59//lmScpxNJEnLli1T165dZbfblZ6errp168pisXj08mTVNj8/P40YMUL//e9/Va9ePUegFBQUlO29c1czLSd5+ewAAAoOUT8AoEiqX7++OnXqpGPHjumf//ynOnXq5KgX07dvX7366qsKCQlx1C2RpPDwcP3555/69NNP1b17dxmGoaSkJF24cEGnT592HLtVq1bat2+fPvzwQ40ePdpRjLiwbdy4UZIcM4xq1Kih33//3fHYy5gxYzR8+HAZhqFu3brpgw8+cKoftXTpUn377beSpP79+ysuLk733HOPGjdurBkzZhRaDamTJ08qPDw8136ZmZk51ivKKhJtdvW1rMcJPQmVsgK9oqZ169aKiYnRBx98oAULFmj79u3q1q2bhg4dqsTERJ0/f15lypTRggUL1LZt21yPZxiGFi5cKOlSmOrO5bVr7Ha76S//We6++27Vq1dPO3fu1OLFi7PN1MlN1jgqVKigkydPZmufM2eOIiMjlZmZmafxFTS73a5169ZJurT6WU61zrIYhqFVq1Zl63P5TLo6deooJSVFKSkpevnll/Xuu+9mO050dLSk/9Vlu9y///1v9e/fX+np6bJYLC5XkbzSgQMHtHz5cqe/ObnJzMx0mgWWlJSk+vXr6/PPP3cE3J7WAQMAFE2ESgCAImv27NmOotWX8+S/1EuXvqwEBwcrODjYqdaOdGmlsX79+unFF1809SWpoMTHx+uXX36R5Dy7wNfXVxcuXFCvXr20ePFiSdLYsWM1ZMgQp/1/+OEHPfPMM5Kkxx9/XM2bN9dXX32lAwcO6MCBA9q5c6eWLFnimAXlibwsVZ+T1157TWlpaerXr1+2L7mXB0W5hSJmxpOUlOT4Mmt2ufi8uNriwZeHnpK0a9cu/ec//9F//vMfLV26VJ988oneffddvffee/r3v/+tY8eOOfq2b99et99+u0fnWbFihQ4dOiQp+3LvV7o8pMnMzMwxVLLb7Y7VBl2FlhUqVFB0dLTWrFmjZs2aeTTOy3n6eJvZAupXc8/MBJwxMTGKjY2VdGnG0eWzjvLi8pXScnqc9ejRo/r555/l6+vr9PuWkJCgnj17auHChY6g2svLS1OnTvXovAsXLtTy5csVGBjoUf9Zs2ZpwIABSkxMVO3atTVy5Ei98sorunDhgjp06KDRo0dryJAhjvcyt5UPC7roPwAgb3j8DQBQZOUUKOU3s4HS4cOHdfz4cSUmJurMmTPatm2bpKv/r+2ff/650tPTFR4e7li9TLo0++i2227T4sWL5e/vr3nz5jkFShcvXtSbb76pRx55RDabTTfffLM+/PBDSZfCpS+++EL+/v7avn27GjRooFWrVuU6lqzCuadOndKJEyeu6rouXryodevW6YcffnDMkLmcmaDIk7pHWWbNmuX4slrQn6M1a9Y4gpW8FBE+fvy4o57W8uXLVbt2bd1xxx16++23tXfvXkeoUrZsWU2YMEFHjhzR66+/7ggG5s+fr6pVq6pfv346d+6c23ONGzdOklStWjWXj0ZlufzeXPne9+vXTx07dtSdd97pKPpds2ZNl8cqXbq0nnrqqWzbs4IddyGNp6vhmX0MzkwIdfn4zp0757jfntT3ueeee0w/XnflK6vQeJMmTZSWlqb4+HgdPnxYAwcOzHa+GTNmyDAMtWvXTiEhIUpLS9P06dNVo0YNLVy4UOHh4VqwYIHH136l3P7WrVq1Sk2bNtXLL7+sxMREVa9eXdHR0ercubN++uknhYWFyW63a8KECdq3b5/jvc0Kgl29sj4rrP4GAEULM5UAAEXeyZMnFRAQIH9/f1mt1lxratjt9hy/MNrtdmVmZiotLU3Jycny9/c3XUtpzZo1OT4u50nxXHemTZsm6VJBbkn6888/1bVrV0cB5ltuuUVfffWVGjVqpMzMTP36669asGCB5s+f7/gyHRYWphUrVuimm25yHPeZZ55RRESEHnnkEf3999966KGH9Mknn6hbt24ux5IVDhiGoSeffFLjx483/ciSdGlmwahRoxQbGysvLy/169cvW5/LZ1qsXbtWLVu2zNanW7dumj9/vstV11auXKlt27bJbrfr/Pnz+uWXXxwr4Pn4+KhGjRqmx+7O3LlzNWzYMBmGodjYWKfQIy+rqf3rX/9yfLG+fLW8mjVr6qGHHnK6n9KlcGnSpEnq06ePBg0apMWLFys1NVXvv/++Pv/8c+3atSvH+zVv3jxHrZ3+/fvnOq7LZwglJyc7FWcuV66cUzHtChUquP1M5XYOdwFP/fr19fHHH7ssDv3YY495VOfnSmZCpUOHDqlBgwYKCQlRbGys47NotraSGR999JG2bNmikydPOupgtWvXTj4+PvLx8ckxEP/zzz8df0uefvppSVLPnj0dq8+1adNGn3/+uaPGUWZmpulA3FUAOHPmTE2ZMkX79u1zbGvUqJG+/fZbx2f4zjvv1A8//KAOHTpo5cqVqlWrluM+DB06VKNHj3Z53ipVqujYsWOmZ6QBAAoWoRIAoMirV6+ezp8/73H/l156SS+99FKu/QYNGqTx48ebGkuXLl30yiuvZKvf8thjj5k6zpW+//573X///Xr++eclXQqRwsPD5eXlpVdffVVjxoxxzLjZvn27OnbsqLi4OMf+99xzjxYuXOhUgDxLo0aNtHHjRj3wwAM6ePCgXn75ZbVo0SLbI4FZHnroIYWHh+vkyZP67bff1Lp166u6Nkk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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "edible_bars = ax.bar(range(10), edible_cc, 0.4, color='#DEB887')\n",
    "poison_bars = ax.bar([x + 0.4 for x in range(10)], poisonous_cc, 0.4, color='#778899')\n",
    "\n",
    "ax.set_xlabel(\"蘑菇帽颜色\", fontsize=20)\n",
    "ax.set_ylabel('数量', fontsize=20)\n",
    "ax.set_title('基于颜色的可食用和有毒蘑菇', fontsize=22)\n",
    "ax.set_xticks([x + 0.4 / 2 for x in range(10)])\n",
    "ax.set_xticklabels(ccx,fontsize=12)\n",
    "ax.legend((edible_bars, poison_bars), ('可食用', '有毒'), fontsize=17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f50a0af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['none', 'foul', 'fishy', 'spicy', 'almond', 'anise', 'pungent', 'creosote', 'musty']\n",
      "[3528, 2160, 576, 576, 400, 400, 256, 192, 36]\n"
     ]
    }
   ],
   "source": [
    "odor = df['odor'].value_counts()\n",
    "ox = odor.axes[0].tolist()\n",
    "oy = odor.values.tolist()\n",
    "print(ox)\n",
    "print(oy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "590ea969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[120, 2160, 576, 576, 0, 0, 256, 192, 36]\n",
      "[3408, 0, 0, 0, 400, 400, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "edible_oder = []\n",
    "poisonous_odor=[]\n",
    "t = [list(i) for i in zip(ox,oy)]\n",
    "for i in t:\n",
    "    edible = len(df[(df['odor']==i[0]) & (df['Class'] == 'edible')].index)\n",
    "    poisonous = i[1]-edible\n",
    "    edible_oder.append(edible)\n",
    "    poisonous_odor.append(poisonous)\n",
    "print(poisonous_odor)\n",
    "print(edible_oder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11648a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "edible_bars = ax.bar(range(9), edible_oder, 0.4, color='#DEB887')\n",
    "poison_bars = ax.bar([x + 0.4 for x in range(9)], poisonous_odor, 0.4, color='#778899')\n",
    "\n",
    "ax.set_xlabel(\"气味\", fontsize=20)\n",
    "ax.set_ylabel('数量', fontsize=20)\n",
    "ax.set_title('基于气味的可食用和有毒蘑菇', fontsize=22)\n",
    "ax.set_xticks([x + 0.4 / 2 for x in range(9)])\n",
    "ax.set_xticklabels(ox,fontsize=12)\n",
    "ax.legend((edible_bars, poison_bars), ('可食用', '有毒'), fontsize=17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "114c9b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x3000 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_correlation(df,name):\n",
    "    df['Class'].replace({'edible':0, 'poisonous':1},inplace=True)\n",
    "    return df.groupby([name])['Class'].sum()/ df[name].value_counts()\n",
    "\n",
    "plt.close()\n",
    "plt.figure(figsize=(16,30))\n",
    "i = 1\n",
    "correlation = []\n",
    "for name in df.columns.tolist()[1:]:\n",
    "    result = get_correlation(df[['Class',name]],name)\n",
    "    ax = plt.subplot(6,4,i)\n",
    "    result.plot(kind='bar',color='#778899')\n",
    "    ax.set_title(name, fontsize=16)\n",
    "    temp = result[result>0.75]\n",
    "    correlation.append(temp)\n",
    "    i+=1\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, left=0.05, right=0.95, hspace=0.4, wspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af0dcb72-b610-46d7-9f4c-2ead535d901f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[cap-shape\n",
       " conical    1.0\n",
       " dtype: float64,\n",
       " cap-surface\n",
       " grooves    1.0\n",
       " dtype: float64,\n",
       " Series([], dtype: float64),\n",
       " Series([], dtype: float64),\n",
       " odor\n",
       " creosote    1.0\n",
       " fishy       1.0\n",
       " foul        1.0\n",
       " musty       1.0\n",
       " pungent     1.0\n",
       " spicy       1.0\n",
       " dtype: float64,\n",
       " Series([], dtype: float64),\n",
       " Series([], dtype: float64),\n",
       " gill-size\n",
       " narrow    0.88535\n",
       " dtype: float64,\n",
       " gill-color\n",
       " buff     1.0\n",
       " green    1.0\n",
       " dtype: float64,\n",
       " Series([], dtype: float64),\n",
       " Series([], dtype: float64),\n",
       " stalk-surface-above-ring\n",
       " silky    0.939292\n",
       " dtype: float64,\n",
       " stalk-surface-below-ring\n",
       " silky    0.9375\n",
       " dtype: float64,\n",
       " stalk-color-above-ring\n",
       " brown       0.964286\n",
       " buff        1.000000\n",
       " cinnamon    1.000000\n",
       " yellow      1.000000\n",
       " dtype: float64,\n",
       " stalk-color-below-ring\n",
       " brown       0.875\n",
       " buff        1.000\n",
       " cinnamon    1.000\n",
       " yellow      1.000\n",
       " dtype: float64,\n",
       " Series([], dtype: float64),\n",
       " veil-color\n",
       " yellow    1.0\n",
       " dtype: float64,\n",
       " ring-number\n",
       " none    1.0\n",
       " dtype: float64,\n",
       " ring-type\n",
       " large    1.0\n",
       " none     1.0\n",
       " dtype: float64,\n",
       " spore-print-color\n",
       " chocolate    0.970588\n",
       " green        1.000000\n",
       " white        0.758794\n",
       " dtype: float64,\n",
       " Series([], dtype: float64),\n",
       " habitat\n",
       " paths    0.881119\n",
       " dtype: float64]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efcb978e",
   "metadata": {},
   "source": [
    "# 数据清洗及特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "21f00c7e-c5f9-4f22-ad58-2efab493c5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       equal\n",
       "1        club\n",
       "2        club\n",
       "3       equal\n",
       "4       equal\n",
       "        ...  \n",
       "8119     club\n",
       "8120     club\n",
       "8121     club\n",
       "8122     club\n",
       "8123     club\n",
       "Name: stalk-root, Length: 8124, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.fillna(method='pad')\n",
    "df['stalk-root']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d654e32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class                       0\n",
       "cap-shape                   0\n",
       "cap-surface                 0\n",
       "cap-color                   0\n",
       "bruises?                    0\n",
       "odor                        0\n",
       "gill-attachment             0\n",
       "gill-spacing                0\n",
       "gill-size                   0\n",
       "gill-color                  0\n",
       "stalk-shape                 0\n",
       "stalk-root                  0\n",
       "stalk-surface-above-ring    0\n",
       "stalk-surface-below-ring    0\n",
       "stalk-color-above-ring      0\n",
       "stalk-color-below-ring      0\n",
       "veil-type                   0\n",
       "veil-color                  0\n",
       "ring-number                 0\n",
       "ring-type                   0\n",
       "spore-print-color           0\n",
       "population                  0\n",
       "habitat                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7fd55281",
   "metadata": {},
   "outputs": [],
   "source": [
    "lab_col = ['Class','bruises?','gill-attachment','gill-spacing','gill-size','stalk-shape','veil-type','ring-number']\n",
    "labelencoder=LabelEncoder()\n",
    "for i in lab_col:\n",
    "    df[i]=labelencoder.fit_transform(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "930b56bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "non_label_cols = [col for col in df.columns if col not in lab_col]  \n",
    "non_label_df = df[non_label_cols]  \n",
    "  \n",
    "enc = OneHotEncoder(sparse_output=False)  \n",
    "non_label_encoded = enc.fit_transform(non_label_df)  \n",
    "  \n",
    "col_names = []  \n",
    "for i, col in enumerate(non_label_cols):  \n",
    "    categories = enc.categories_[i]  \n",
    "    for cat in categories:  \n",
    "        col_names.append(f\"{col}_{cat}\")  \n",
    "encoded_df = pd.DataFrame(non_label_encoded, columns=col_names, index=df.index)  \n",
    "final_df = pd.concat([df[lab_col], encoded_df], axis=1)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "806ce150",
   "metadata": {},
   "source": [
    "# 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4b936ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = final_df['Class']\n",
    "x = final_df.drop('Class', axis=1) \n",
    "x_train,x_test,y_train,y_test = train_test_split(x, y, test_size=0.3, random_state=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026b3a23",
   "metadata": {},
   "source": [
    "# 决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9fc533b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = DecisionTreeClassifier().fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "99c95724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree is: 1.0\n",
      "最优分类器: {'criterion': 'entropy', 'max_depth': 7, 'min_samples_split': 8} 最优分数: 1.0\n",
      "分类结果报告 \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1268\n",
      "           1       1.00      1.00      1.00      1170\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_p = tree.predict(x_test)\n",
    "print('The accuracy of the Decision Tree is:',accuracy_score(y_test,y_p))\n",
    "\n",
    "param = {'criterion':['entropy'],'max_depth':list(range(7,12)),'min_samples_split':list(range(8,12))}\n",
    "grid = GridSearchCV(DecisionTreeClassifier(),param_grid=param,cv=5)#寻找最优参数\n",
    "grid.fit(x_train,y_train)\n",
    "print('最优分类器:',grid.best_params_,'最优分数:', grid.best_score_)#最优参数下1次交叉验证的结果\n",
    "classifyreport = classification_report(y_test, y_p)\n",
    "print('分类结果报告 \\n', classifyreport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "959d41d2",
   "metadata": {},
   "outputs": [
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       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>8&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M645.49,-698.95C664.22,-687.5 684.49,-674.9 703,-663 703.92,-662.41 704.86,-661.8 705.79,-661.2\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"707.87,-664.02 714.33,-655.62 704.04,-658.16 707.87,-664.02\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<path fill=\"#e5813a\" stroke=\"black\" d=\"M574,-544C574,-544 422,-544 422,-544 416,-544 410,-538 410,-532 410,-532 410,-473 410,-473 410,-467 416,-461 422,-461 422,-461 574,-461 574,-461 580,-461 586,-467 586,-473 586,-473 586,-532 586,-532 586,-538 580,-544 574,-544\"/>\n",
       "<text text-anchor=\"start\" x=\"418\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">cap&#45;surface_grooves ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"462.5\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.007</text>\n",
       "<text text-anchor=\"start\" x=\"449.5\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2371</text>\n",
       "<text text-anchor=\"start\" x=\"447.5\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2363, 8]</text>\n",
       "<text text-anchor=\"start\" x=\"470.5\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>9&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M550.24,-579.91C544.16,-571.01 537.66,-561.51 531.39,-552.33\"/>\n",
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       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<path fill=\"#95caf1\" stroke=\"black\" d=\"M771.5,-544C771.5,-544 616.5,-544 616.5,-544 610.5,-544 604.5,-538 604.5,-532 604.5,-532 604.5,-473 604.5,-473 604.5,-467 610.5,-461 616.5,-461 616.5,-461 771.5,-461 771.5,-461 777.5,-461 783.5,-467 783.5,-473 783.5,-473 783.5,-532 783.5,-532 783.5,-538 777.5,-544 771.5,-544\"/>\n",
       "<text text-anchor=\"start\" x=\"612.5\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ring&#45;type_evanescent ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"658.5\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.433</text>\n",
       "<text text-anchor=\"start\" x=\"653\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 41</text>\n",
       "<text text-anchor=\"start\" x=\"647\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [13, 28]</text>\n",
       "<text text-anchor=\"start\" x=\"666.5\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>9&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M618.25,-579.91C627.42,-570.65 637.26,-560.73 646.7,-551.21\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"649.27,-553.59 653.83,-544.02 644.3,-548.66 649.27,-553.59\"/>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>11</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M449.5,-425C449.5,-425 310.5,-425 310.5,-425 304.5,-425 298.5,-419 298.5,-413 298.5,-413 298.5,-354 298.5,-354 298.5,-348 304.5,-342 310.5,-342 310.5,-342 449.5,-342 449.5,-342 455.5,-342 461.5,-348 461.5,-354 461.5,-354 461.5,-413 461.5,-413 461.5,-419 455.5,-425 449.5,-425\"/>\n",
       "<text text-anchor=\"start\" x=\"306.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">cap&#45;shape_conical ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"344.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.003</text>\n",
       "<text text-anchor=\"start\" x=\"331.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2367</text>\n",
       "<text text-anchor=\"start\" x=\"329.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2363, 4]</text>\n",
       "<text text-anchor=\"start\" x=\"352.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>10&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M457.06,-460.91C447.72,-451.65 437.72,-441.73 428.12,-432.21\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"430.43,-429.58 420.87,-425.02 425.5,-434.55 430.43,-429.58\"/>\n",
       "</g>\n",
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       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M562.5,-417.5C562.5,-417.5 491.5,-417.5 491.5,-417.5 485.5,-417.5 479.5,-411.5 479.5,-405.5 479.5,-405.5 479.5,-361.5 479.5,-361.5 479.5,-355.5 485.5,-349.5 491.5,-349.5 491.5,-349.5 562.5,-349.5 562.5,-349.5 568.5,-349.5 574.5,-355.5 574.5,-361.5 574.5,-361.5 574.5,-405.5 574.5,-405.5 574.5,-411.5 568.5,-417.5 562.5,-417.5\"/>\n",
       "<text text-anchor=\"start\" x=\"499\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"489.5\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"487.5\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4]</text>\n",
       "<text text-anchor=\"start\" x=\"499.5\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>10&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M508.06,-460.91C510.74,-450.09 513.65,-438.38 516.36,-427.44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"519.77,-428.22 518.78,-417.67 512.98,-426.53 519.77,-428.22\"/>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>12</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M364.5,-306C364.5,-306 271.5,-306 271.5,-306 265.5,-306 259.5,-300 259.5,-294 259.5,-294 259.5,-235 259.5,-235 259.5,-229 265.5,-223 271.5,-223 271.5,-223 364.5,-223 364.5,-223 370.5,-223 376.5,-229 376.5,-235 376.5,-235 376.5,-294 376.5,-294 376.5,-300 370.5,-306 364.5,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"278.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gill&#45;size ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"282.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.003</text>\n",
       "<text text-anchor=\"start\" x=\"269.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2366</text>\n",
       "<text text-anchor=\"start\" x=\"267.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2363, 3]</text>\n",
       "<text text-anchor=\"start\" x=\"290.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
       "</g>\n",
       "<!-- 11&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>11&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M358.49,-341.91C353.87,-333.2 348.95,-323.9 344.17,-314.89\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"347.25,-313.22 339.47,-306.02 341.06,-316.5 347.25,-313.22\"/>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>17</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M477.5,-298.5C477.5,-298.5 406.5,-298.5 406.5,-298.5 400.5,-298.5 394.5,-292.5 394.5,-286.5 394.5,-286.5 394.5,-242.5 394.5,-242.5 394.5,-236.5 400.5,-230.5 406.5,-230.5 406.5,-230.5 477.5,-230.5 477.5,-230.5 483.5,-230.5 489.5,-236.5 489.5,-242.5 489.5,-242.5 489.5,-286.5 489.5,-286.5 489.5,-292.5 483.5,-298.5 477.5,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"414\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"404.5\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"402.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"414.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\n",
       "</g>\n",
       "<!-- 11&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>11&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M401.51,-341.91C407.36,-330.87 413.7,-318.9 419.6,-307.77\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"422.83,-309.14 424.42,-298.67 416.65,-305.86 422.83,-309.14\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>13</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M298.5,-179.5C298.5,-179.5 205.5,-179.5 205.5,-179.5 199.5,-179.5 193.5,-173.5 193.5,-167.5 193.5,-167.5 193.5,-123.5 193.5,-123.5 193.5,-117.5 199.5,-111.5 205.5,-111.5 205.5,-111.5 298.5,-111.5 298.5,-111.5 304.5,-111.5 310.5,-117.5 310.5,-123.5 310.5,-123.5 310.5,-167.5 310.5,-167.5 310.5,-173.5 304.5,-179.5 298.5,-179.5\"/>\n",
       "<text text-anchor=\"start\" x=\"224\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"203.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2227</text>\n",
       "<text text-anchor=\"start\" x=\"201.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2227, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"224.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>12&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M295.1,-222.91C288.81,-211.76 281.99,-199.66 275.66,-188.44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"278.67,-186.66 270.71,-179.67 272.57,-190.1 278.67,-186.66\"/>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>14</title>\n",
       "<path fill=\"#e6843d\" stroke=\"black\" d=\"M427,-187C427,-187 341,-187 341,-187 335,-187 329,-181 329,-175 329,-175 329,-116 329,-116 329,-110 335,-104 341,-104 341,-104 427,-104 427,-104 433,-104 439,-110 439,-116 439,-116 439,-175 439,-175 439,-181 433,-187 427,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"341.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">bruises? ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"348.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.042</text>\n",
       "<text text-anchor=\"start\" x=\"339\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 139</text>\n",
       "<text text-anchor=\"start\" x=\"337\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [136, 3]</text>\n",
       "<text text-anchor=\"start\" x=\"356.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>12&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M340.9,-222.91C345.81,-214.2 351.06,-204.9 356.14,-195.89\"/>\n",
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       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>15</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M359.5,-68C359.5,-68 288.5,-68 288.5,-68 282.5,-68 276.5,-62 276.5,-56 276.5,-56 276.5,-12 276.5,-12 276.5,-6 282.5,0 288.5,0 288.5,0 359.5,0 359.5,0 365.5,0 371.5,-6 371.5,-12 371.5,-12 371.5,-56 371.5,-56 371.5,-62 365.5,-68 359.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"296\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"286.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"284.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 3]</text>\n",
       "<text text-anchor=\"start\" x=\"296.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>14&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M361.66,-103.73C356.91,-95.06 351.89,-85.9 347.11,-77.18\"/>\n",
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       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M488,-68C488,-68 402,-68 402,-68 396,-68 390,-62 390,-56 390,-56 390,-12 390,-12 390,-6 396,0 402,0 402,0 488,0 488,0 494,0 500,-6 500,-12 500,-12 500,-56 500,-56 500,-62 494,-68 488,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"417\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"400\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 136</text>\n",
       "<text text-anchor=\"start\" x=\"398\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [136, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"417.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
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       "<!-- 14&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>14&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M406.71,-103.73C411.54,-95.06 416.65,-85.9 421.5,-77.18\"/>\n",
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       "<title>20</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M704,-417.5C704,-417.5 626,-417.5 626,-417.5 620,-417.5 614,-411.5 614,-405.5 614,-405.5 614,-361.5 614,-361.5 614,-355.5 620,-349.5 626,-349.5 626,-349.5 704,-349.5 704,-349.5 710,-349.5 716,-355.5 716,-361.5 716,-361.5 716,-405.5 716,-405.5 716,-411.5 710,-417.5 704,-417.5\"/>\n",
       "<text text-anchor=\"start\" x=\"637\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"624\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13</text>\n",
       "<text text-anchor=\"start\" x=\"622\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [13, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"637.5\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n",
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       "<!-- 19&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>19&#45;&gt;20</title>\n",
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       "<g id=\"node22\" class=\"node\">\n",
       "<title>21</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M824,-417.5C824,-417.5 746,-417.5 746,-417.5 740,-417.5 734,-411.5 734,-405.5 734,-405.5 734,-361.5 734,-361.5 734,-355.5 740,-349.5 746,-349.5 746,-349.5 824,-349.5 824,-349.5 830,-349.5 836,-355.5 836,-361.5 836,-361.5 836,-405.5 836,-405.5 836,-411.5 830,-417.5 824,-417.5\"/>\n",
       "<text text-anchor=\"start\" x=\"757\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"744\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 28</text>\n",
       "<text text-anchor=\"start\" x=\"742\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 28]</text>\n",
       "<text text-anchor=\"start\" x=\"757.5\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\n",
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       "<!-- 19&#45;&gt;21 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>19&#45;&gt;21</title>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x2a47a239b50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "features = list(x_train.columns)\n",
    "dot_data = export_graphviz(tree,out_file=None,\n",
    "                           feature_names = features,\n",
    "                           max_depth=7,\n",
    "                           class_names=['1','0'],\n",
    "                           filled=True,rounded=True,\n",
    "                           special_characters=True\n",
    "                          )\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c9d9ab",
   "metadata": {},
   "source": [
    "# 朴素贝叶斯"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1b93c9c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of GaussianNB: 0.9150943396226415\n",
      "分类结果报告 \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.84      0.91      1268\n",
      "           1       0.85      1.00      0.92      1170\n",
      "\n",
      "    accuracy                           0.92      2438\n",
      "   macro avg       0.92      0.92      0.91      2438\n",
      "weighted avg       0.93      0.92      0.91      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler  \n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(x_train)  # 拟合训练数据以找到均值和标准差  \n",
    "trainX2 = scaler.transform(x_train)  # 转换训练数据  \n",
    "testX2 = scaler.transform(x_test)    # 转换测试数据  \n",
    "clf = GaussianNB().fit(trainX2, y_train)   \n",
    "pred = clf.predict(testX2)  \n",
    "accuracy = accuracy_score(y_test, pred)  # 注意pred和testY的顺序  \n",
    "  \n",
    "print(\"accuracy of GaussianNB:\", accuracy)  \n",
    "classifyreport = classification_report(y_test, pred)\n",
    "print('分类结果报告 \\n', classifyreport)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09e1bf8",
   "metadata": {},
   "source": [
    "# 神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "07bc2ef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优分类器参数: {'hidden_layer_sizes': (20, 10)}\n",
      "最优交叉验证分数: 1.0\n",
      "测试集上的神经网络分数是: 1.0\n",
      "分类结果报告 \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1268\n",
      "           1       1.00      1.00      1.00      1170\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()  \n",
    "scaler.fit(x_train)  \n",
    "trainX1 = scaler.transform(x_train)  \n",
    "testX1 = scaler.transform(x_test)  \n",
    "param = {'hidden_layer_sizes': [(20, 10), (10, 5)]}\n",
    "\n",
    "grid = GridSearchCV(MLPClassifier(random_state=42),  # 添加 random_state 以确保结果可复现  \n",
    "                        param_grid=param,  \n",
    "                        cv=5,  # 5折交叉验证  \n",
    "                   )\n",
    "grid.fit(trainX1, y_train)  \n",
    "\n",
    "print('最优分类器参数:', grid.best_params_)  \n",
    "print('最优交叉验证分数:', grid.best_score_) \n",
    "\n",
    "test_score = accuracy_score(grid.best_estimator_.predict(testX1), y_test) \n",
    "print(\"测试集上的神经网络分数是:\", test_score) \n",
    "classifyreport = classification_report(y_test, grid.best_estimator_.predict(testX1)) \n",
    "print('分类结果报告 \\n', classifyreport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a907fe66",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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